ARISE-PIE: A People Information Integration Engine over the Web

Searching for people information on the Web is a common practice in life. However, it is time consuming to search for such information manually. In this paper, we aim to develop an automatic people information search system, named ARISE-PIE. To build such a system, we tackle two major technical challenges: data harvesting and data integration. For data harvesting, we study how to leverage search engine to help crawl the relevant Web pages for a target entity; then we propose a novel learning to query model that can automatically select a set of “best” queries to maximize collective utility (e.g., precision or recall). For data integration, we study how to leverage flexible forms of constraints as weak supervision to achieve collective information extraction from a target entity’s Web page corpus; then we propose a novel conditional probabilistic formulation to model constraints and an efficient realization to enable the inference with constraints. We evaluate our data harvesting and data integration solutions on the real-world data sets, and show that they both achieve better performance than the state-of-the-art baselines. We also evaluate our system on a benchmark data set and with a user study, in which we both show promising results.